Specsmaxxing goes viral: YAML writing requirements become a new paradigm for AI programming

Core conclusion

On May 3, 2026, developer Brendan published a popular article titled "Specsmaxxing" on Hacker News, proposing to use YAML format functional specification files (feature.yaml) as the "persistent memory" for AI Agent programming to solve the common "AI schizophrenia" problem in AI code generation. Its open source acai.sh toolkit topped the HN homepage, receiving 85+ likes and 65+ heated discussions.

Key Points

• Event Time: 2026-05-03, the article appeared on the Hacker News homepage #1
• Core concept: Specsmaxxing = Spec (standard) + Maxxing (maximization), which refers to using YAML to write acceptance criteria
• Affected: Developer teams using AI Agents (Claude Code, OpenAI Codex, Cursor, etc.)
• Open source tools: acai.sh — YAML-based specification-driven development toolkit (Elixir + TypeScript)

Background: The "schizophrenia" problem of AI-generated code

Anyone who has written code using AI Agent has experienced this scenario: you ask Claude or GPT to generate a function, and after a few iterations back and forth, the function runs perfectly. Then you close the session and reopen it the next day, and the AI ​​not only forgets yesterday's needs, but also completely forgets the architectural decisions.

The root cause of this phenomenon, which the author calls "AI Psychosis", is not that the model has become stupid, but that the specification (Spec) is not persistent. The developer's needs originally exist in the mind, but AI has no long-term memory.

"The context window is the ceiling. When you fill up the context, or kill the session, the most important details get squashed." — Brendan

The author believes that since the code itself can no longer fully carry the design intent (the code generated by AI lacks "author memory"), the specification must be written in a place that can be read by both AI and humans - a YAML file.

Key Impact

How Acai.sh works

Acai.sh is an open source specification-driven development toolkit developed by Brendan for his company of the same name. It consists of four steps:

1. Specify: Create a .feature.yaml file in the project to describe functional requirements in a declarative format
2. Ship (delivery): AI Agent generates code according to YAML specifications to ensure that each delivery covers all acceptance criteria
3. Review: The code generated by AI automatically performs regression verification against YAML specifications.
4. Iterate: Update the YAML file and regenerate it, no longer relying on "hallucinatory memory"

Adaptation suggestions

For developers who are using AI Agent tools such as Claude Code, OpenAI Codex, Cursor, OpenClaw, etc., you can try the following practices immediately:

• Create a features/ directory in the project and use YAML files to replace dictating requirements
• Each feature.yaml contains: feature name, acceptance criteria list, boundary conditions
• Enter the YAML file as the system prompt context of the AI Agent
• Update the YAML file after each iteration instead of just appending it in the chat window

Quick Start (Example)

# feature/user-authentication.yaml
name: User Authentication
acceptance_criteria:
  - Users can sign up with email and password
  - Password must be at least 8 characters
  - Email verification is sent within 30 seconds
  - Rate limit: max 5 attempts per minute

HN 。**AI **（Institutional Memory），" AI ，"。Specsmaxxing ——****。

Git commit message JIRA ticket ID， ticket 。，****。

• Specsmaxxing — acai.sh
• AI Generated Code Has No Author — ossature.dev

，Claude Code、OpenAI、Claude、ChatGPT、Cursor、OpenClaw、GitHub

Internal link guidance

• Want to learn systematically how to develop with AI Agent? Watch: AI Agent Tools 2026 Complete Tutorial: 5 Tools to Build an Automated Pipeline in 30 Minutes
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